One of the best things about being at DIALOG was the opportunity to meet a bunch of ILOG customers and learn how they are making better decisions in their organizations. It seems to me that every one of these customers is, in a very practical way, helping to build a smarter planet. The first group were a set of optimization customers – Alliance for Paired Donation, WinWorks KK and DecisionWare.
Alliance for Paired donation
This organization is making better decisions about kidney donations. Yes, that’s right, kidney donations. The problem is that lots of people who want to donate a kidney to a loved one who needs it cannot do so – they are incompatible. Finding matched pairs – where one family’s donor matches the other’s recipient and vice verse is hard. Add to this the folks who just donate a kidney to help others and you have a situation fraught with complexity and opportunity. Because donations from living donors last longer than those from posthumous donors, the impact of getting this right is tremendous. The savings are pretty good too – dialysis is expensive. The Alliance for Paired Donation uses ILOG’s optimization technology to find the pattern of donors/recipients that will result in the maximum number of successful operations. Each time a new kidney is offered, the optimization model runs through this constrained problem and finds the best solution – the one that will improve, perhaps save, the most lives. This works so well that they can sometimes use a single donation to trigger 10 successful transplants. Not a classic use of optimization, but a fascinating one.
Winworks is another optimization customer with a more classic focus – workforce scheduling. Winworks supports retailers, hotels and others with large workforces and handles work scheduling and forecasting. Using the optimization engine they are able to help clients use fewer work hours to get the job done and ensure that work is allocated more fairly to temporary and contract staff.
A Colombian company, DecisionWare is bringing supply chain optimization models to South America, where they are significantly less common. Most companies in South America do not have an operations research or decision sciences group and so are disadvantaged when it comes to competing with companies in countries where these groups are common. DecisionWare offers optimization-based decision support for companies to use in negotiations, with other companies or with unions, and in planning every step of their supply chain.
These optimization customers were all interesting – nice to see a range of solutions using optimization. The other customers were rules customers.
One of ING’s operations is managing define benefit pensions. Companies with these pensions typically outsource the management of them completely and ING handles everything from tax and legal issues to answering questions for benefits holders. The complexity of the situation comes from conflicting regulations (department of labor, IRS etc) as well as from the acquisition and divestment of businesses covered by the pensions. Their legacy system for calculating benefits used to take 18 months to change to handle a new customer – not exactly a compelling time frame. Back in 2002 they re-imagined the system and used ILOG’s rules to replace the procedural benefit calculation engine on their mainframe with a rules-based one that could be managed by business users. Requiring only the same kind of skill as using Excel, they completely redid the calculation engine. Adding business user-friendly regression testing and test harnesses, they empowered the business to define the rules completely. Even a complex client now takes only 6-9 months to get on-board. A great example of empowering the business to own rules that are complex, but complex in business terms not technical ones.
Travelers uses the ILOG rules products to manage small commercial underwriting. Before they started on this project in 2006 they relied on manual decision making by their independent agents. This meant that two businesses might get the same rate (because they are apparently similar small businesses) even though their risk profiles were quite different. This leads to being adversely selected against because the better risk can find a better price elsewhere leaving Travelers with only the worse risk. Travelers implemented a decision management solution that uses business rules and predictive analytics in concert to automate the underwriting decision more comprehensively. Not only did they boost automated underwriting from 17% to 70% they also increased the number of attributes being considered in the price to around 40. This improves accuracy and risk management tremendously. This system is credited with helping them increase their quotes per agent by 26%, increase the number of agents who want to quote Travelers’ policies by 19% and drive an overall increase of 50% in new business quotes. I liked this one because so many of the insurance underwriting cases are for home and auto insurance and this one was for the more complex small business policies, showing the increasing scope for business rules.
Equifax is best known as a data company and provides financial data to companies in the financial industry, telecoms and others. wanted to turn this data into decisions for their clients to add value while maximizing the use of their data and implemented ILOG rules. They have 100M txns a day so performance was critical. These transactions are often short running – the company calling equifax might only have 10-15 seconds and so Equifax is often only given 1-2 seconds.They have more than 100 companies who maintain their own risk management rules and scorecards in these services. this allows their customers, for instance, to change their risk rules the night before “Black Friday”! Something they would never have considered before. This is the kind of agility that can make a real difference – just in time responses to critical events.
I blogged about RCI earlier They use ILOG BRMS for member visibility and inventory segmentation (35 years of contractual obligations), pricing (more than 50M calculations daily), reservations, exchange fees, discounting, communication rules and more for both members and business partners. Combining these rules with a search engine builds customer trust because they never see a property in the search that they won’t be able to exchange or rent – the search finds the properties but the rules narrow it based on the member and their constraints. The rule engine constantly interacts with the search so that all the business rules that are relevant are executed as the search is being conducted – not after the fact but as “part of” the search. Revenue analysts, marketing and IT operations people enter rules and all these rules get pushed into the rule engine. This then sits behind all the channels providing consistent, accurate results.
British Airways first saw the need for rules in their website. Starting as a brochureware site this has evolved into the single largest source of bookings for BA. In the process it has become rather rigid and hard to change. This lack of agility is exacerbated by a lack of confidence in any change being correct which drives a very long and complex regression test before anything can be deployed. To bring business agility to the website while also improving the other systems that interact with customers and partners, BA invested in ILOG’s rules platform and built a high-availability, high performance platform for use by the website and other systems. Their model is to develop lots of small, focused decision services that can be easily managed and changed by different business units. by deploying these on a robust platform they are able t deliver agility while meeting their performance and reliability goals. One example is an upsell offer made to those with bookings who return to the site. This has gone from a very fixed offer to a much more dynamic and carefully tuned one, particularly important in the current highly constrained market for business class tickets. The formal release process is now just two weeks from business initiation to implementation, increasing the agility of even core systems, and the flexibility of the rules-based decision services helps manage risk when changes are made. For instance, when they discovered a problem with part of a major release they were able to simply add rules to exclude that particular aspect of the change and go ahead with the rest of the deployment. Before the whole release would have been delayed – now they have fine-grained control delivered by rules at lots of different flex points in the architecture.
Optimal workforce schedules and supply chains, perfectly matched kidney donors, business ownership of complex systems with thousands of rules, agility with the confidence to make changes whenever they are needed and consistent, accurate decisions delivered across channels. Truly better decisions and steps towards a smarter planet.